Physical Analytics - Precision Agriculture

Description: Agriculture has been performed in one way or another for thousands of years. The development of agriculture has allowed humans to settle, build villages, and with enough surplus food eventually build great cities. In modern times, agriculture has become much more productive due to mechanization, fertilization, pest control, and selective breeding of crops and animals. We are arguably in the midst a third agricultural revolution right now: precision agriculture.

Precision Agriculture was sparked very recently by the availability of high-resolution satellite imagery and hand-held GPS systems that let farmers know exactly where within a field their machines are at any given time. In combination with sensors on the ground or on machines (e.g. yield monitors on combines), this allows much more targeted field management. For example, previously an entire field may have been sprayed to combat a pests, whereas with precision agriculture, only those parts of the field that actually need it will be sprayed. Precision agriculture addresses the large variabilities in essential physical properties that exist within one farmed field, such as soil nutrients, soil water-holding capacity, topography, plant growing stage, etc. No longer are management processes applied uniformly over the entire field. They are instead applied on a sub-field level in management zones, optimizing yield, while at the same time minimizing fertilizer, pesticide, and water input. This saves the famer money and protects the environment by requiring less irrigation and limiting chemical runoff from the field.

PAIRS, IBM's geospatial analytics platform, is uniquely positioned for applications in precision agriculture due to the many different datasets that are all pre-aligned and easily queried. Layer include multispectral satellite images, weather measurements and forecasts, soil maps, land-use layers, vegetation index maps, topography layers, and evaporation and transpiration products. Some of these, such as topography or land use, are quasi static, while others such as satellite or weather are acquired frequently, highlighting the changes on the field over time.

PAIRS layer example 1: The Normalized Difference Vegetation Index (NDVI) is an example of a satellite-derived layer in PAIRS. It makes use of two of the multispectral satellite bands (red and near-infrared) and combines them in a way that makes it very sensitive to chlorophyll levels in plants. In general, the higher the NDVI, the more vigorous the vegetation. This allows in-season predictions of yield, and also the continued observation or identification of problem zones, where additional fertilizer or irrigation might be beneficial.

PAIRS layer example 2: Potential Evapotranspiration (ET0) is a layer derived from weather forecasts, that models the expected evaporation and transpiration from plant surfaces. Plants use water to build biomass, but most of the water absorbed by the roots (95%) is actually transpired in the leaves to keep the plant cool and vigorous on hot days. ET0 is a measure for how much evaporation and transpiration will occur on a particular day if enough water is available in the soil. We use ET0 in combination with NDVI maps for building irrigation recommendations in various setting.